The Curious Case of the Opera-Writing AI
"Let me tell you about the time Claude wrote me an entire opera libretto..."
That was how I chose to introduce this series in Article 1, telling the curious story of how my prompt about an "opera written by an AI" resulted in Claude delivering an original five-act, multi-page opera script.
I was amazed. How did Claude construct a cohesive narrative arc, witty banter between lead sopranos and tenors, and emotionally punctuated songs, all from my musing, "I wonder what an opera written by Claude would look like?"
Claude used the full context of my prompt - including the implications of words like "opera," "five acts," "duets," "songs" - to appropriately manifest a tailored response. This story illustrates how contextual understanding is at the heart of AI's communicative capabilities. Claude parsed both the linguistic and situational framework I conveyed through subtext, terminology, and the invitation to be creative.
In this article, we'll explore exactly how context shapes the phenomenal ability of large language models to generate speech. How does training methodology instill context sensitivity in LLMs like Claude or ChatGPT? Why does ambiguity stump these models, and what progress is being made? Understanding context remains the key to unlocking the potential of AI while avoiding the pitfalls of misinterpretation.
How context shapes language generation
Natural language generation is the most visible capability of large language models. Given a textual prompt, LLMs can generate coherent, multi-paragraph responses tailored to the topical and contextual cues of the prompt. Modern models approach conversational ability in certain domains by generating increasingly relevant, nuanced language as users provide more context.
So how do LLMs acquire this capacity for context-aware generation unmatched by previous NLP systems? The answer lies in scale and self-supervised learning from exposure to massive textual datasets. By ingesting more than a trillion words from disparate sources, Claude and other LLMs learn statistical patterns about how words relate to each other based on neighboring terms. This enables the intuitive construction of language that reflects the likely sequence of words given the surrounding context.
For example, Claude learns from web data that "cat" and "feline" are more closely associated with words like "pet," "purr," "pounce" than with unrelated terms like "utensil" or "hydrogen. So when I provide context about "my cute purring cat," Claude responds accordingly with relevant word choices.
Large datasets allow Claude and other llms to learn regularities in context-dependent word usage across topics such as politics, science, dialogue, and more. Claude also learns more subtle nuances - that "not good" conveys negativity, while "not bad" suggests positivity. Such inferences help to shape tone and style appropriately for generational tasks.
Training Methodology and Context Sensitivity
In addition to scale, advances in model architecture and training techniques also improve context handling in modern LLMs. Approaches such as memory, sparse attention, and contiguous blocking help Claude learn relationships between more distant words in text. This improves discourse coherence in long-form generation.
Pre-training goals have also evolved to promote stronger contextualization. For example, some models train by predicting randomly omitted words based on surrounding terms. Correctly estimating omitted words forces reliance on semantic and syntactic context. Other tricks, such as training on longer contiguous passages of text, further reinforce context assimilation.
New techniques also allow providing Claude with external context beyond the prompt itself. For example, explicit input of domain knowledge about opera before asking Claude to generate a libretto. This provides Claude with topical background to generate more appropriate responses. We'll explore such contextual priming techniques in more detail later in this series.
But first, how do LLMs deal with highly nuanced contextual challenges like ambiguity and subtext?
Ambiguity, Subtext, and Nuanced Language
Despite advances, large language models still struggle with highly nuanced linguistic phenomena that are relatable to us humans. Double entendres, sarcasm, metaphor, ambiguity - these leave AI guessing without sufficient context. Claude may excel at conversing in limited domains, but still stumbles in complex, intent-laden dialogues.
For example, Claude interprets the question "Can AI be sarcastic?" quite literally. He responds by earnestly discussing AI's inability so far to convey complex emotions beyond sincerity and randomness. On the other hand, most people would infer my subtext there. "Can AI be sarcastic?" sarcastically implies an observation about how bad Claude is at recognizing sarcasm!
These quirks are partly due to the mismatch between LLMs' training data and real-world conversational contexts. But they also reveal gaps in the ability to disambiguate words with multiple meanings, or to resolve ambiguous pronouns to what they refer.
Common ambiguous phrases also trip up LLMs, depending on the context that influences interpretation. For example, experiential context determines whether "fan flies around stadium" refers to an enthusiastic spectator or a mechanical device!
Without properly grounded context, generated text runs the risk of being inaccurate or nonsensical. Access to situated dialog history helps humans deal with utterances that lack explicit context. We need to better equip LLMs with similar capabilities, as we explore in later articles.
In closing, however, it is undeniably impressive how far LLMs have come thanks to scaling model sizes and training data. And exciting innovations focused specifically on improving LLMs' use of context are just over the horizon...
But first, how exactly does the context within all the different training content that Claude and other LLMs ingest affect their comprehension? We unpack this next in article 3!
Glossary
- Language generation: The ability of LLMs to produce original, coherent text continuations conditioned on a user prompt, query, or example.
- Self-supervised learning: A training approach where LLMs learn by predicting masked or missing words in passages based on surrounding context.
source : https://www.researchgate.net/figure/Self-supervised-language-and-vision-models-learn-representations-by-imputing-data_fig1_358259713- Discourse coherence: When LLMs can connect concepts and maintain logical flow across long-form text with multiple sentences or paragraphs.
- Pre-training objectives: Specialized tasks used to train LLMs, like masked word prediction and next sentence prediction, meant to teach useful linguistic capacities.
- External context: Additional context beyond the prompt itself provided explicitly to the LLM, like relevant domain knowledge about a topic, to aid generation.
- Ambiguity: When language has multiple potential meanings that depend greatly on context to disambiguate the intended interpretation.
- Subtext: Underlying ideas, emotions, or intents implicit within explicit statements. Sarcasm, metaphors, and double entendres demonstrate subtext LLMs currently struggle perceiving.
- Grounding: The integration of context into LLMs' reasoning, such as pertinent background knowledge about named entities and discourse history, to reduce ambiguity and improve relevance.